Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements  

作　　者：Sheng-Ming Xu Dong Dong Wei Li Tian Bai Ming-Zhu Zhu Gui-Shan Gu 

机构地区：[1]Department of Orthopedic Surgery,The First Hospital of Jilin University,Changchun 130000,Jilin Province,China [2]Department of Radiology,The First Hospital of Jilin University,Changchun 130000,Jilin Province,China [3]College of Computer Science and Technology,Jilin University,Changchun 130000,Jilin Province,China

出　　处：《World Journal of Clinical Cases》2023年第7期1477-1487,共11页世界临床病例杂志

摘　　要：BACKGROUND Femoral trochlear dysplasia(FTD)is an important risk factor for patellar instability.Dejour classification is widely used at present and relies on standard lateral X-rays,which are not common in clinical work.Therefore,magnetic resonance imaging(MRI)has become the first choice for the diagnosis of FTD.However,manually measuring is tedious,time-consuming,and easily produces great variability.AIM To use artificial intelligence(AI)to assist diagnosing FTD on MRI images and to evaluate its reliability.METHODS We searched 464 knee MRI cases between January 2019 and December 2020,including FTD(n=202)and normal trochlea(n=252).This paper adopts the heatmap regression method to detect the key points network.For the final evaluation,several metrics(accuracy,sensitivity,specificity,etc.)were calculated.RESULTS The accuracy,sensitivity,specificity,positive predictive value and negative predictive value of the AI model ranged from 0.74-0.96.All values were superior to junior doctors and intermediate doctors,similar to senior doctors.However,diagnostic time was much lower than that of junior doctors and intermediate doctors.CONCLUSION The diagnosis of FTD on knee MRI can be aided by AI and can be achieved with a high level of accuracy.

关 键 词：Femoral trochlear dysplasia Deep learning Artificial intelligence Magnetic resonance imaging DIAGNOSIS 

分 类 号：R445.2[医药卫生—影像医学与核医学] R681.8[医药卫生—诊断学]